repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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rainyear/pytips | Tips/2016-05-01-Class-and-Metaclass-i.ipynb | mit | def who(obj):
print(id(obj), type(obj))
who(1)
who(None)
who(who)
"""
Explanation: Python 类与元类的深度挖掘 I
上一篇介绍了 Python 枚举类型的标准库，除了考虑到其实用性，还有一个重要的原因是其实现过程是一个非常好的学习、理解 Python 类与元类的例子。因此接下来两篇就以此为例，深入挖掘 Python 中类与元类背后的机制。
翻开任何一本 Python 教程，你一定可以在某个位置看到下面这两句话：
Python 中一切皆为对象（Everything in Python is an object）；
Python... |
GoogleCloudPlatform/training-data-analyst | quests/data-science-on-gcp-edition1_tf2/09_cloudml/flights_model_tf2.ipynb | apache-2.0 | %pip --version
import tensorflow as tf
import sys
print(tf.version.VERSION)
print('{}.{}'.format(sys.version_info.major,sys.version_info.minor))
#%pip install --user --upgrade --quiet pip
#%pip install --user --upgrade --quiet tf_nightly-2.0-preview
"""
Explanation: Developing, Training, and Deploying a TensorFlow ... |
ambonip/Analisi-Trab | Analisi thibya Imm2000.ipynb | gpl-3.0 | %matplotlib inline
#importo le librerie
import pandas as pd
import os
from __future__ import print_function,division
import numpy as np
import seaborn as sns
os.environ["NLS_LANG"] = "ITALIAN_ITALY.UTF8"
"""
Explanation: <h2>Analisi comparativa dei metodi di dosaggio degli anticorpi anti recettore del TSH</h2>
<h3>Met... |
patemotter/trilinos-prediction | ml_files/pandas-profiling.ipynb | mit | import pandas_profiling
import pandas as pd
"""
Explanation: This script generates profiles of pandas dataframes using the Pandas-Profiling library
End of explanation
"""
props = pd.read_csv('../data/processed_properties.csv',
header=0, index_col=0)
props = props.drop_duplicates()
props = props.... |
hongguangguo/shogun | doc/ipython-notebooks/classification/Classification.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from modshogun import *
#Needed lists for the final plot
classifiers_linear = []*10
classifiers_non_linear = []*10
classifiers_names = []*10
fadings = []*10
"""
Explanation: Visual Comparison Between Different Classification Methods in Shogun
Noteb... |
albahnsen/ML_SecurityInformatics | notebooks/14-KaggleCompetition.ipynb | mit | import pandas as pd
import zipfile
with zipfile.ZipFile('../datasets/fraud_transactions_kaggle.csv.zip', 'r') as z:
f = z.open('fraud_transactions_kaggle.csv')
data = pd.read_csv(f, index_col=0)
data.head()
data.tail()
data.fraud.value_counts(dropna=False)
"""
Explanation: 14 - Kaggle Competition
Fraud Dete... |
liganega/Gongsu-DataSci | previous/y2017/Wextra/GongSu26_Pandas_Introduction_3.ipynb | gpl-3.0 | from GongSu24_Pandas_Introduction_1 import *
"""
Explanation: Pandas 소개 3
GonsSu24 내용에 이어서 Pandas 라이브러리를 소개한다.
먼저 GongSu24를 임포트 한다.
End of explanation
"""
obj = Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])
obj
new_obj = obj.drop('c')
new_obj
obj.drop(['d', 'c'])
"""
Explanation: 5.2.2 하나의 로우 또는 칼럼 제외하기:... |
hvillanua/deep-learning | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
corochann/chainer-hands-on-tutorial | src/01_chainer_intro/chainer_module2.ipynb | mit | # Initial setup following http://docs.chainer.org/en/stable/tutorial/basic.html
import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chain... |
yvesdubief/UVM-ME249-CFD | ME249-Pb1-steady-laminar-channel-flow.ipynb | gpl-2.0 | PDF('figures/channel.pdf',size=(300,600))
"""
Explanation: <h1>Steady Laminar Channel Flow</h1>
The objective of this first assignment is to compute the well-known Poiseuille velocity profile in a channel flow. Assuming that the channel length and width are both very large, the governing equations for an incompressib... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 2 Supervised learning/Homework/5 data preparation and logistic regression/Preprocessing_LR.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
matplotlib.style.use('ggplot')
%matplotlib inline
"""
Explanation: Предобработка данных и логистическая регрессия для задачи бинарной классификации
Programming assignment
В задании вам будет предложено ознакомиться с основным... |
ChadFulton/statsmodels | examples/notebooks/chi2_fitting.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.api as sm
"""
Explanation: Least squares fitting of models to data
This is a quick introduction to statsmodels for physical scientists (e.g. physicists, astronomers) or engineers.
Why is this needed?
Because most of statsmodels was written by statisticians and ... |
ZuckermanLab/NMpathAnalysis | test/example_mfpts_chignolin.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pyemma
import mdtraj as md
from glob import glob
"""
Explanation: Generic PyEMMA analysis pipeline for DESRES folding trajectories
This notebook illustrates how to perform a simple folding analysis using the PyEMMA pa... |
atcemgil/notes | Regression.ipynb | mit | # http://matplotlib.org/1.5.1/examples/pylab_examples/anscombe.html
from __future__ import print_function
"""
Edward Tufte uses this example from Anscombe to show 4 datasets of x
and y that have the same mean, standard deviation, and regression
line, but which are qualitatively different.
"""
import matplotlib.pyplot... |
guyk1971/deep-learning | embeddings/Skip-Gram word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
sylvchev/coursIntroPython | cours/2-ApprendrePython-ControleExecution.ipynb | gpl-3.0 | x = int(input("Entrez un entier : "))
if x < 0:
x = 0
print('Négatif changé en zéro')
elif x == 0:
print('Zéro')
elif x == 1: print ('Un seul')
else:
print('Plus')
"""
Explanation: Contrôles d'exécution
A part l’instruction while que l’on vient de découvrir, Python comprend les instructions de con... |
quantopian/research_public | notebooks/data/eventvestor.buyback_auth/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.eventvestor import buyback_auth as dataset
# or if you want to import the free dataset, use:
#from quantopian.data.eventvestor import buyback_auth_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
import mat... |
GoogleCloudPlatform/practical-ml-vision-book | 05_create_dataset/05_audio.ipynb | apache-2.0 | !gsutil cp gs://ml-design-patterns/audio_train/00353774.wav cello.wav
!gsutil cp gs://ml-design-patterns/audio_train/001ca53d.wav sax.wav
import matplotlib.pyplot as plt
from scipy import signal
from scipy.io import wavfile
import numpy as np
fig, ax = plt.subplots(2, 2, figsize=(15, 10))
for idx, instr in enumerate(... |
Unidata/unidata-python-workshop | notebooks/CF Conventions/NetCDF and CF - The Basics.ipynb | mit | # Import some useful Python tools
from datetime import datetime, timedelta
import numpy as np
# Twelve hours of hourly output starting at 22Z today
start = datetime.utcnow().replace(hour=22, minute=0, second=0, microsecond=0)
times = np.array([start + timedelta(hours=h) for h in range(13)])
# 3km spacing in x and y
... |
McWilliamsCenter/CMUDeepLens | notebooks/BLF_GroundBased.ipynb | mit | import sys
sys.path.append('..')
%load_ext autoreload
%autoreload 2
%pylab inline
"""
Explanation: Strong Lens Finding Challenge
In this notebook, we illustrate how to use CMU DeepLens to classify ground based images from the strong lens finding challenge http://metcalf1.difa.unibo.it/blf-portal/gg_challenge.html
End ... |
saashimi/code_guild | interactive-coding-challenges/stacks_queues/n_stacks/n_stacks_challenge.ipynb | mit | class Stacks(object):
def __init__(self, num_stacks, stack_size):
# TODO: Implement me
pass
def abs_index(self, stack_index):
# TODO: Implement me
pass
def push(self, stack_index, data):
# TODO: Implement me
pass
def pop(self, stack_index):
# T... |
kylepolich/dataskeptic | shownotes/the-elbow-method.ipynb | cc0-1.0 | %matplotlib inline
import os
import json
import pickle
import geocoder
import math
import random
import pandas as pd
import numpy as np
from scipy.spatial import cKDTree
import matplotlib.pyplot as plt
addresses = {}
root = '../data/'
allfiles = []
dirs = os.listdir(root)
for d in dirs:
files = os.listdir(root + ... |
mne-tools/mne-tools.github.io | 0.22/_downloads/96cdfef13f5475e2f32b862fa94904b7/plot_visualize_stc.ipynb | bsd-3-clause | import os
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from mne import read_evokeds
data_path = sample.data_path()
sample_dir = os.path.join(data_path, 'MEG', 'sample')
subjects_dir... |
TareasJF/INF393 | T1/Tarea1.ipynb | mit | import pandas as pd
import numpy as np
df = pd.read_csv("kc_house_data.csv")
df.drop(['id','date','zipcode',],axis=1,inplace=True)
df.head()
"""
Explanation: 1.- Regresión Lineal Ordinaria (LSS)
En esta sección trabajaremos con un dataset conocido como House Sales in King County, USA, presentado
en la plataforma de Ka... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/pos_stl.ipynb | apache-2.0 | !pip install hanlp -U
"""
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/pos_stl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Ope... |
ZhiangChen/deep_learning | thesis/supervised_learning/Supervised Object Classification.ipynb | mit | import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
import matplotlib.pyplot as plt
import random
import operator
import time
import os
import math
"""
Explanation: Supervised Object Classification
Zhiang Chen, March 2017
- Xazier initialization
- Dropout
- Rectified linear units
- Leaky... |
streety/biof509 | Wk09-Advanced-ML-tasks-Deep-Learning.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib inline
plt.gray()
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
fig, axes = plt.subplots(3,5, figsize=(12,8))
for i, ax in enumerate(axes.flatten()):
ax.imshow(X_train[i], interpolation='nearest')
plt.show()
from keras.models ... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session04/Day0/TooBriefMLSolutions.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Introduction to Machine Learning:
Examples of Unsupervised and Supervised Machine-Learning Algorithms
Version 0.1
Broadly speaking, machine-learning methods constitute a diverse collection of data-driven algorithms designed to class... |
Paradigm4/wearable_prototypes | sleep.ipynb | agpl-3.0 | from scidbpy import connect
import getpass
import requests
import warnings
warnings.filterwarnings("ignore")
requests.packages.urllib3.disable_warnings(requests.packages.urllib3.exceptions.InsecureRequestWarning)
db = connect(scidb_url="https://localhost:8083",
scidb_auth=('root', getpass.getpass('Please ... |
mathnathan/notebooks | dissertation/KL Upper Bound and Applications.ipynb | mit | import numpy as np
import matplotlib
#matplotlib.use('nbagg')
import matplotlib.pyplot as plt
x = np.linspace(1e-6,1,200)
y1 = -np.log(x)
y2 = -5*x+5
plt.plot(x,y1,label='$-\log p(x)$',lw=2)
plt.plot(x,y2,label='$-5p(x)+5$',lw=2)
plt.xlabel("$p(x)$")
plt.ylim(-0.5,10)
plt.tight_layout()
plt.legend()
plt.show()
"""
E... |
isendel/machine-learning | ml-regression/week 6/week-6-local-regression-assignment-blank.ipynb | apache-2.0 | import graphlab
"""
Explanation: Predicting house prices using k-nearest neighbors regression
In this notebook, you will implement k-nearest neighbors regression. You will:
* Find the k-nearest neighbors of a given query input
* Predict the output for the query input using the k-nearest neighbors
* Choose the be... |
jasontlam/snorkel | tutorials/workshop/Workshop_2_Writing_Labeling_Functions.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import re
import sys
import numpy as np
# Connect to the database backend and initalize a Snorkel session
from lib.init import *
from lib.scoring import *
from lib.lf_factories import *
from snorkel.lf_helpers import test_LF
from snorkel.annotations import load_g... |
karlstroetmann/Artificial-Intelligence | Python/6 Classification/Spam-Detection.ipynb | gpl-2.0 | import os
import re
import math
"""
Explanation: Spam Detection Using the Naive Bayes Algorithm
The process of creating a spam detector using the naive Bayes algorithm is split up into four steps.
Create a set of the most common words occurring in spam and ham (i.e. non-spam) emails.
For every word occurring in this... |
scotthuang1989/Python-3-Module-of-the-Week | networking/TCPIP Client and Server.ipynb | apache-2.0 | # %load socket_echo_server.py
import socket
import sys
# Create a TCP/IP socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# Bind the socket to the port
server_address = ('localhost', 10000)
print('starting up on {} port {}'.format(*server_address))
sock.bind(server_address)
# Listen for incoming conne... |
meli-lewis/osb2015 | OSB2015_intro_data.ipynb | mit | from __future__ import division
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn
import rpy2
from IPython.display import display, Image, YouTubeVideo
%matplotlib inline
"""
Explanation: bit.ly/osb2015pandas
Introduction to data munging with Jupyter and p... |
w4zir/ml17s | lectures/lec04-linear-regression-example.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
# read data in pandas frame
dataframe = pd.read_csv('datasets/example1.csv', encoding='utf-8')
# assign x and y
X = np.array(dataframe[['x']])
y = np.array(dataframe[['y']])
m = y.size # number ... |
DJCordhose/ai | notebooks/rl/berater_chh_181104.ipynb | mit | # !pip install -e git+https://github.com/openai/baselines#egg=berater
"""
Explanation: <a href="https://colab.research.google.com/github/DJCordhose/ai/blob/master/notebooks/rl/berater_chh_181104.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Berate... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/Objects and Data Structures Assessment Test-Solution.ipynb | apache-2.0 | # Your answer is probably different
(20000 - (10 ** 2) / 12 * 34) - 19627.75
"""
Explanation: Objects and Data Structures Assessment Test
Test your knowledge.
Answer the following questions
Write a brief description of all the following Object Types and Data Structures we've learned about:
For the full answers, rev... |
jdhp-docs/python-notebooks | python_sklearn_decision_trees_fr.ipynb | mit | from sklearn import tree
"""
Explanation: Les arbres de décision avec Python/Scikit-learn
http://scikit-learn.org/stable/modules/tree.html
End of explanation
"""
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
clf.predict([[2., 2.]])
clf.predict_proba([[2., 2.]])
"""
Expla... |
linhbngo/cpsc-4770_6770 | 19-message-oriented-middleware-producer.ipynb | gpl-3.0 | !cd ~/software/kafka_2.11-1.0.0; \
./bin/kafka-topics.sh --zookeeper localhost:2181 --delete --topic test
!cd ~/software/kafka_2.11-1.0.0; \
./bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test
!cd ~/software/kafka_2.11-1.0.0; \
./bin/kafka-topics.sh -... |
ES-DOC/esdoc-jupyterhub | notebooks/csir-csiro/cmip6/models/sandbox-3/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CSIR-CSIRO
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Transport... |
abulbasar/machine-learning | Time Series - Stock Price Forecast using ARIMA.ipynb | apache-2.0 | df = pd.read_csv("https://raw.githubusercontent.com/abulbasar/data/master/tcs-stock.csv")
df.head()
"""
Explanation: Download stock price data from NSE website. Here I download stock price data for TCS stock.
End of explanation
"""
df[df["Date"].duplicated(keep = False)]
"""
Explanation: Check whether you have dupl... |
jrbourbeau/cr-composition | notebooks/learning-curve.ipynb | mit | import argparse
from collections import defaultdict
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn.apionly as sns
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score, learning_curve
import co... |
arkottke/pysra | examples/example-12.ipynb | mit | import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pysra
%matplotlib inline
plt.rcParams["figure.dpi"] = 150
"""
Explanation: Compare impact of frequency dependent $D_{min}$
End of explanation
"""
plast_indices = [0, 20, 50, 100]
stresses_mean = 101.3 * np.array([0.5, ... |
mjysci/ud-dlnd-side-proj | p2_image_classification/image_classification.ipynb | gpl-3.0 | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
soloman817/udacity-ml | misc/DLND-your-first-network/dlnd-your-first-neural-network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
grigorisg9gr/menpo-notebooks | menpo/Transforms/Piecewise_Affine.ipynb | bsd-3-clause | import numpy as np
from menpo.transform import PiecewiseAffine
"""
Explanation: Piecewise Affine Transforms
End of explanation
"""
from menpo.shape import TriMesh, PointCloud
a = np.array([[0, 0], [1, 0], [0, 1], [1, 1],
[-0.5, -0.7], [0.8, -0.4], [0.9, -2.1]])
b = np.array([[0,0], [2, 0], [-1, 3], [2,... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session01/Day2/IntroStat.ipynb | mit | fig = plt.figure(1,figsize=(8,8))
for ii in range(1, 10):
rv = scipy.stats.norm.rvs(0, 1, size = 200)
ax = fig.add_subplot(3,3,ii)
sm.qqplot(rv, line = 's', ax = ax)
ax.set_xlabel('')
ax.set_ylabel('')
fig.text(0.5, 0.02, 'Theoretical Quantiles', ha='center',size=16)
fig.text(0.02, 0.5, 'Observed Qu... |
antoniomezzacapo/qiskit-tutorial | community/hello_world/quantum_8ball.ipynb | apache-2.0 | # begin with importing essential libraries for IBM Q
from qiskit import IBMQ
from qiskit import ClassicalRegister, QuantumRegister, QuantumCircuit
"""
Explanation: <img src="../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" ... |
openfisca/openfisca-france-indirect-taxation | openfisca_france_indirect_taxation/examples/notebooks/compare_depenses_transports.ipynb | agpl-3.0 | from __future__ import division
import pkg_resources
import os
import pandas as pd
from pandas import concat
import seaborn
"""
Explanation: L'objectif des "compare" est d'évaluer la qualité des calages effectués. Ils comparent les dépenses ou quantités agrégées de Budget des Familles après calage, avec celles de la... |
ToqueWillot/M2DAC | FDMS/TME3/Model_V2.ipynb | gpl-2.0 | # from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
%matplotlib inline
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
import scipy.stats as stats
# Sk cheats
from sklearn.cross_val... |
acdarby/Python_lectures | Lecture_2.ipynb | gpl-3.0 | exam_scores = [67,78,94,45,55,66]
print("scores: " ,exam_scores)
"""
Explanation: Lecture 2 – Lists, conditionals and loops
Recap on Variables
A variable
named cell of memory storing a single value
Assigned a value using the equals symbol
Can be given any name you like
Except no spaces and cannot start with a number... |
JaggedParadigm/pyplearnr | .ipynb_checkpoints/data_science_lib_demo-checkpoint.ipynb | apache-2.0 | import pandas as pd
df = pd.read_pickle('trimmed_titanic_data.pkl')
df.info()
"""
Explanation: pyplearnr demo
Here I demonstrate pyplearnr, a wrapper for building/training/validating scikit learn pipelines using GridSearchCV or RandomizedSearchCV.
Quick keyword arguments give access to optional feature selection (e.... |
MichalBusta/clstm | misc/lstm-delay.ipynb | apache-2.0 | net = clstm.make_net_init("lstm1","ninput=1:nhidden=4:noutput=2")
print net
net.setLearningRate(1e-4,0.9)
print clstm.network_info(net)
"""
Explanation: Network creation and initialization is very similar to C++:
networks are created using the make_net(name) factory function
the net.set(key,value) method is used to ... |
arcyfelix/Courses | 18-11-22-Deep-Learning-with-PyTorch/02-Introduction to PyTorch/Part 5 - Inference and Validation.ipynb | apache-2.0 | import torch
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Download and load t... |
vikasgorur/notebooks | cs229/Linear Regression.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.preprocessing import scale
"""
Explanation: CS229: Lecture 2
Linear Regression, Gradient Descent
In this notebook we implement some of the concepts discussed in Lecture 2 of CS229 - Machine Le... |
LSSTDESC/Twinkles | examples/notebooks/Sort_Objects_in_ICs.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Sorting Objects in Instance Catalogs
Bryce Kalmbach
This notebook provides a series of commands that take a Twinkles Phosim Instance Catalog and creates different pandas dataframes for different types of objects in the catalog. It first separates the full sets of... |
mne-tools/mne-tools.github.io | stable/_downloads/ab9aa98c1c3d10ded0b9447abaef0b61/30_ecog.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Chris Holdgraf <choldgraf@gmail.com>
# Adam Li <adam2392@gmail.com>
# Alex Rockhill <aprockhill@mailbox.org>
# Liberty Hamilton <libertyhamilton@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import ma... |
macks22/gensim | docs/notebooks/topic_coherence_tutorial.ipynb | lgpl-2.1 | from __future__ import print_function
import os
import logging
import json
import warnings
try:
import pyLDAvis.gensim
CAN_VISUALIZE = True
pyLDAvis.enable_notebook()
from IPython.display import display
except ImportError:
ValueError("SKIP: please install pyLDAvis")
CAN_VISUALIZE = False
impo... |
madsenmj/ml-introduction-course | Class04/Class04.ipynb | apache-2.0 | import pandas as pd
import seaborn as sns
sns.set_style("white")
#Note the new use of the dtype option here. We can directly tell pandas to use the Speed column as a category in one step.
speeddf = pd.read_csv("Class04_speed_data.csv",dtype={'Speed':'category'})
lm = sns.lmplot(x='Grade', y='Bumpiness', data=speeddf,... |
NYUDataBootcamp/Projects | UG_S16/Mammon-Obesity.ipynb | mit | %matplotlib inline
import pandas as pd
import pandas.io.data as web
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
"""
Explanation: Are income and Childhood Obesity Correlated?
Jennifer Mammon
May 2016
Childhood Obesity in the USA
"Overweight" is defined as having excess body weight for a par... |
diegocavalca/Studies | deep-learnining-specialization/1. neural nets and deep learning/week4/Building+your+Deep+Neural+Network+-+Step+by+Step+v5.ipynb | cc0-1.0 | import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v3 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['imag... |
amitkaps/applied-machine-learning | reference/Module-02c-reference.ipynb | mit | #Load the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#Default Variables
%matplotlib inline
plt.rcParams['figure.figsize'] = (16,9)
plt.rcParams['font.size'] = 18
plt.style.use('fivethirtyeight')
pd.set_option('display.float_format', lambda x: '%.2f' % x)
#Load the dataset
df = pd... |
vinaykumar2491/Lumos | Project_Python/py_bootcamp/decorators.ipynb | apache-2.0 | def func():
return 1
func()
s = 'this is a global variable'
def func():
print locals()
print globals()['s']
func()
def hello(name = 'rustom'):
return 'Hello ' + name
hello()
greet = hello
greet
greet()
# del hello
greet
greet()
hello()
greet2 = hello()
greet2
greet2()
del hello
greet2
gr... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_spm_faces_dataset.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
import mne
from mne.datasets import spm_face
from mne.preprocessing import ICA, create_eog_epochs
from mne impor... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/structured/labs/4c_keras_wide_and_deep_babyweight.ipynb | apache-2.0 | import datetime
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
print(tf.__version__)
"""
Explanation: LAB 4c: Create Keras Wide and Deep model.
Learning Objectives
Set CSV Columns, label column, and column defaults
Make dataset of features and label from CSV files
... |
fermiPy/fermipy-extra | notebooks/dSphs.ipynb | bsd-3-clause | pipe = Pipeline(linkname = 'dSphs')
configfile = 'config/master_dSphs.yaml'
"""
Explanation: First we build a pipeline object to analyze the dSphs, and point it at the config file for the dSphs analysis.
End of explanation
"""
pipe.preconfigure(configfile)
"""
Explanation: We have to 'preconfigure' the pipeline, b... |
sdpython/ensae_teaching_cs | _doc/notebooks/exams/td_note_2020_1.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.e - Enoncé 22 octobre 2019 (1)
Correction du premier énoncé de l'examen du 22 octobre 2019. L'énoncé propose une façon de disposer des tables rondes dans une salle ronde.
End of explanation
"""
def distance_table(x1, y1, x2, y2):
... |
evanmiltenburg/python-for-text-analysis | Assignments/ASSIGNMENT-2.ipynb | apache-2.0 | for number in range(4, 0, -1): # change 4 to 99 when you're done with debugging
print(number, 'bottles of beer on the wall,')
"""
Explanation: Assignment 2: Containers
Deadline: Tuesday, September 21, 2021 before 20:00
Please name your files:
ASSIGNMENT_2_FIRSTNAME_LASTNAME.ipynb
assignment2_utils.py
Please s... |
gvasold/gdp17 | basics/basics_3.ipynb | apache-2.0 | # TODO
"""
Explanation: Strings revisited
Vorbemerkung
In diesem Notebook verwenden wir den Text "Der Mann von vierzig Jahren" von Jakob Wassermann. Dieser Text stammt aus dem Jahr 1913 und wurde vom Projekt Gutenberg digitalisiert (http://www.gutenberg.org/ebooks/15736). Um die Arbeit mit dem Text zu erleichtern, hab... |
kmyokoyama/mathings | euler_method.ipynb | apache-2.0 | def euler(x_0, y_0, f, step=0.001, k_max=None):
r"""
Euler's method for solving first-order ODE.
The function computes `k_max` iterations from the initial conditions `x_0` and `y_0` with
steps of size `step`. It yields a total of `k_max` + 1 values. The recorrent equation is:
y_{k+1} = y_{... |
qutip/qutip-notebooks | examples/bloch_sphere_with_colorbar.ipynb | lgpl-3.0 | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
from qutip import *
from qutip.ipynbtools import version_table
%matplotlib inline
"""
Explanation: Adding a colorbar to a Bloch Sphere
End of explanation
"""
delta = 0.5 * 2 *... |
ledeprogram/algorithms | class7/donow/Gruen_Gianna_7_donow.ipynb | gpl-3.0 | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
"""
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot... |
kubeflow/kfp-tekton-backend | components/gcp/dataproc/create_cluster/sample.ipynb | apache-2.0 | %%capture --no-stderr
KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
"""
Explanation: Name
Data processing by creating a cluster in Cloud Dataproc
Label
Cloud Dataproc, cluster, GCP, Cloud Storage, KubeFlow, Pipeline
Summary
A Kubeflow Pipelin... |
Bollegala/bollegala.github.io | lect/dm/PCA.ipynb | mit | import numpy
"""
Explanation: Let us consider four two-dimensional data points x1 = (1,1), x2 = (1, -1), x3 = (-1, -1), and x4 = (-1,1). Use PCA to project these four data points into an one-dimensional space.
End of explanation
"""
X = numpy.zeros((2, 4), dtype=float)
X[:, 0] = numpy.array([1, 1])
X[:, 1] = numpy.a... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.04 find largest or smallest n items.ipynb | gpl-3.0 | import heapq
nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2]
print(heapq.nlargest(3, nums))
print(heapq.nsmallest(3, nums))
"""
Explanation: Previous
1.4 查找最大或最小的N个元素
问题
怎样从一个集合中获得最大或者最小的 N 个元素列表？
解决方案
heapq 模块有两个函数：nlargest() 和 nsmallest() 可以完美解决这个问题。
End of explanation
"""
portfolio = [
{'name': 'IBM', 'share... |
AtmaMani/pyChakras | islr/verifying_central_limit_theorem.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Verifying Central Limit Theorem
The Central Limit Theorem states that the sampling distribution of the sampling means approaches a normal distribution as the sample size gets larger — no mat... |
g-weatherill/catalogue_toolkit | notebooks/Catalogue Querying Example.ipynb | agpl-3.0 | %matplotlib inline
import os, sys
import numpy as np
import matplotlib.pyplot as plt
from eqcat.parsers.isf_catalogue_reader import ISFReader
import eqcat.catalogue_query_tools as cqt
"""
Explanation: Catalogue Query Tools
This notebook provides examples on how to use the Catalogue Toolkit to build and explore a catal... |
chapagain/kaggle-competitions-solution | Titanic: Machine Learning from Disaster/Titanic.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set() # setting seaborn default for plots
"""
Explanation: TITANIC SOLUTION
A BEGINNER'S GUIDE
Loading Modules
End of explanation
"""
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
""... |
pdamodaran/yellowbrick | examples/iguk1987/Yellowbrick_Tour.ipynb | apache-2.0 | import numpy as np
import pandas as pd
%matplotlib inline
from cycler import cycler
import matplotlib.style
import matplotlib as mpl
mpl.style.use('seaborn-white')
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from sklearn.cluster import KMeans
from sklearn.linear_model import RidgeCV
from sklear... |
hektor-monteiro/curso-python | aula-1.ipynb | gpl-2.0 | print "Hello World!"
x=1
# vendo os valores de x
print (x)
print x
x
"""
Explanation: Livro base
http://www-personal.umich.edu/~mejn/computational-physics/
Programando em Python
O primeiro passo é aprender a programar em python
Python é uma linguagem de alto nível, poderosa e muito abrangente
vamos nos limitar aos a... |
GoogleCloudPlatform/training-data-analyst | quests/rl/a2c/a2c_on_gcp.ipynb | apache-2.0 | !pip install tensorflow==2.1 --user
"""
Explanation: Policy Gradients and A2C
In the <a href="../dqn/dqns_on_gcp.ipynb">previous notebook</a>, we learned how to use hyperparameter tuning to help DQN agents balance a pole on a cart. In this notebook, we'll explore two other types of alogrithms: Policy Gradients and A2C... |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/tutorials/01-intro-to-python.ipynb | gpl-3.0 | for x in list(range(5)):
print("One number per loop..")
print(x)
if x > 2:
print("The number is greater than 2")
print("----------------------------")
"""
Explanation: Introduction to Python
Goals:
Learn basic Python operations
Understand differences in data structures
Get familiarized wi... |
IST256/learn-python | content/lessons/05-Functions/LAB-Functions.ipynb | mit | import math
dir(math)
"""
Explanation: Class Coding Lab: Functions
The goals of this lab are to help you to understand:
How to use Python's built-in functions in the standard library.
How to write user-defined functions
How to use other people's code.
The benefits of user-defined functions to code reuse and simplicit... |
anilcs13m/MachineLearning_Mastering | week-4-ridge-regression-assignment-2-blank.ipynb | gpl-2.0 | import graphlab
import numpy as np
"""
Explanation: Regression Week 4: Ridge Regression (gradient descent)
In this notebook, you will implement ridge regression via gradient descent. You will:
* Convert an SFrame into a Numpy array
* Write a Numpy function to compute the derivative of the regression weights with respe... |
splicemachine/splice-community-sample-code | twimlcon-workshop-materials/3 - Model Training.ipynb | apache-2.0 | #Begin spark session
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
#Create pysplice context. Allows you to create a Spark dataframe using our Native Spark DataSource
from splicemachine.spark import PySpliceContext
splice = PySpliceContext(spark)
#Initialize our Feature Store API
fr... |
jsjol/GaussianProcessRegressionForDiffusionMRI | notebooks/compute_ODF.ipynb | bsd-3-clause | btable = np.loadtxt(get_data('dsi515btable'))
#btable = np.loadtxt(get_data('dsi4169btable'))
gtab['test'] = gradient_table(btable[:, 0], btable[:, 1:],
big_delta=gtab['train'].big_delta, small_delta=gtab['train'].small_delta)
gtab['test'].info
"""
Explanation: For the testing we will us... |
Z0m6ie/Zombie_Code | Data_Science_Course/Michigan Data Analysis Course/0 Introduction to Data Science in Python/Week2/Week+2.ipynb | mit | import pandas as pd
pd.Series?
animals = ['Tiger', 'Bear', 'Moose']
pd.Series(animals)
numbers = [1, 2, 3]
pd.Series(numbers)
animals = ['Tiger', 'Bear', None]
df = pd.Series(animals)
df['number_column'] = -99999
df
numbers = [1, 2, None]
pd.Series(numbers)
import numpy as np
np.nan == None
np.nan == np.nan
np.i... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_channel_epochs_image.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Visualize channel over epochs as an ima... |
ahoyosid/ReNA | Example_Neuroimaging.ipynb | bsd-3-clause | from nilearn import datasets
dataset = datasets.fetch_haxby(subjects=1)
import numpy as np
from nilearn.input_data import NiftiMasker
masker = NiftiMasker(mask_strategy='epi', smoothing_fwhm=6, memory='cache')
X_masked = masker.fit_transform(dataset.func[0])
X_train = X_masked[:100, :]
X_data = masker.inverse_transf... |
analysiscenter/dataset | examples/tutorials/04_linear_models_example.ipynb | apache-2.0 | import sys
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
# the following line is not required if BatchFlow is installed as a python package.
sys.path.append('../..')
from batchflow import Dataset, V, F, B, action, Batch... |
davidhamann/python-fmrest | examples/fetching_container_data.ipynb | mit | import fmrest
fms = fmrest.Server('https://10.211.55.15',
user='admin',
password='admin',
database='Contacts',
layout='Demo',
verify_ssl=False
)
fms.login()
"""
Explanation: Fetching container data w... |
aakashm301/Workshop | Refactored_Py_DS_ML_Bootcamp-master/11-Linear-Regression/03-Linear Regression Project - Solutions.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Linear Regression Project - Solutions
Congratulations! You just got some contract work with an Ecommerce com... |
WMD-group/sulfur-model | parameterisation.ipynb | gpl-3.0 | import ase.db
import ase.thermochemistry
from scipy import constants
from sulfur import cm_to_hz, reference_energy
#from sulfur.core import get_potentials, unpack_data, reference_energy, solve_composition
db_file = sulfur.__path__[0] + '/../data/sulfur_pbe0_96.json'
ref_energy = reference_energy(db_file)
c = ase.db.con... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_dipole_fit.ipynb | bsd-3-clause | from os import path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_ave = op.... |
seinberg/deep-learning | weight-initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
laurent-george/tutmom | mystic.ipynb | bsd-3-clause | %matplotlib inline
"""
Explanation: Optimiztion with mystic
End of explanation
"""
"""
Example:
- Minimize Rosenbrock's Function with Nelder-Mead.
- Plot of parameter convergence to function minimum.
Demonstrates:
- standard models
- minimal solver interface
- parameter trajectories using retall... |
Hvass-Labs/TensorFlow-Tutorials | 21_Machine_Translation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import math
import os
"""
Explanation: TensorFlow Tutorial #21
Machine Translation
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
Tutorial #20 showed how to use a Recurrent Neural Network (RNN) to do ... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/automl/showcase_automl_image_classification_batch.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: AutoML image classification model for batch prediction
<table align=... |
landmanbester/fundamentals_of_interferometry | 2_Mathematical_Groundwork/2_3_fourier_series.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
2. Mathematical Groundwork
Previous: 2.2 Important functions
Next: 2.4 The Fourier Transform
Import standard modules:
End of expla... |
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